Deep generative neural networks have shown significant progress over the last years. The main architectures for generation are: (i) VAE [1]} based, for example, NVAE [2]} and VQ-VAE [3]}, (ii) GAN [4]} based, for example, StyleGAN [5]} for vision application and WaveGAN [6]} for speech
(iii) Flow-based, for example Glow [7]} (iv) Autoregessive, for example, Wavenet for speech [8]} and (v) Diffusion Probabilistic Models [9]}, for example, Denoising Diffusion Probabilistic Models (DDPM) [10]} and its implicit version DDIM [11]}.
